Add Quantum Field Theory (QFT) module implementing Wilson's renormali…

README.md

@@ -439,7 +439,7 @@ details = watcher.distances(initial_model, trained_model)
 
 ---
 
-#### compatability with version 0.2.x
+#### Compatibility with version 0.2.x
 
 The new 0.4.x version of WeightWatcher treats each layer as a single, unified set of eigenvalues.
 In contrast, the 0.2.x versions split the Conv2D layers into n slices, one for each receptive field.
@@ -543,9 +543,14 @@ This tool is based on state-of-the-art research done in collaboration with UC Be
 <summary>
 WeightWatcher has been featured in top journals like JMLR and Nature:	
 </summary>
-#### Latest papers and talks
+
+### Latest papers and talks
+
+- [Grokking and Generalization Collapse: Insights from HTSR theory (available upon request)]
 
-- [SETOL: A Semi-Empirical Theory of (Deep) Learning] (in progress)
+- [SETOL: A Semi-Empirical Theory of (Deep) Learning (draft)] (https://github.com/CalculatedContent/setol_paper/blob/main/setol_draft.pdf)
+
+- [Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training (NeurIPS 2023 Spotlight Paper)(https://arxiv.org/abs/2312.00359)
 
 - [Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics](https://arxiv.org/abs/2106.00734)
 
@@ -591,7 +596,11 @@ and has been presented at Stanford, UC Berkeley, KDD, etc:
 - [KDD 2019 Workshop: Statistical Mechanics Methods for Discovering
   Knowledge from Production-Scale Neural Networks](https://dl.acm.org/doi/abs/10.1145/3292500.3332294)
 
-- [KDD 2019 Workshop: Slides](https://www.stat.berkeley.edu/~mmahoney/talks/dnn_kdd19_fin.pdf) 
+- [KDD 2019 Workshop: Slides](https://www.stat.berkeley.edu/~mmahoney/talks/dnn_kdd19_fin.pdf)
+
+#### NeurIPS 2023 
+- [Heavy-Tailed Self-Regularization in Deep Neural Networks](https://neurips.cc/virtual/2023/83033)
+
 
 </details>
 
@@ -600,7 +609,7 @@ and has been presented at Stanford, UC Berkeley, KDD, etc:
 WeightWatcher has also been featured at local meetups and many popular podcasts
 </summary>
 
-#### Popular Popdcasts and Blogs
+#### Popular Podcasts and Blogs
 
 - [This Week in ML](https://twimlai.com/meetups/implicit-self-regularization-in-deep-neural-networks/)
 
@@ -618,16 +627,36 @@ WeightWatcher has also been featured at local meetups and many popular podcasts
 
 - [Applied AI Community](https://www.youtube.com/watch?v=xLZOf2IDLkc&feature=youtu.be)
 
+- [UCL Financial Computing (2022)](https://www.youtube.com/watch?v=sOXROWJ70Pg)
+
 - [Practical AI](https://changelog.com/practicalai/194)
 
-- [Latest Results](https://www.youtube.com/watch?v=rojbXvK9mJg)
+- [AI Nation 2023](https://www.youtube.com/watch?v=rojbXvK9mJg)
+
+- [ICCF 2024](https://youtu.be/_c0-_ru0sZc)
+
+- [Data Science at Home (2025)](https://www.youtube.com/watch?v=iv7Pv3StHms)
+
+- [Cohere for AI 2025](https://www.youtube.com/watch?v=NXqO4nDNIwo)
+
+- [The FreeStyle Podcast](https://www.youtube.com/watch?v=hb0YrwQ3K2Q)
+
+- [This Week in ML AI Podcast](https://twimlai.com/podcast/twimlai/grokking-generalization-collapse-and-the-dynamics-of-training-deep-neural-networks/)
+
+
+  and many more 
+
+
 
 #### 2021 Short Presentations
 
 - [MLC Research Jam  March 2021](presentations/ww_5min_talk.pdf)
 
 - [PyTorch2021 Poster  April 2021](presentations/pytorch2021_poster.pdf)
 
+#### TEDx Talk
+- [The Emergence of Signatures of Artificial General Intelligence ](https://www.youtube.com/watch?v=5dBEzqTlq-Y)
+
 #### Recent talk(s) by Mike Mahoney, UC Berekely
 
 - [IARAI, the Institute for Advanced Research in Artificial Intelligence](https://www.youtube.com/watch?v=Pirni67ZmRQ)

docs/QFT_Module_Documentation.md

@@ -0,0 +1,192 @@
+# WeightWatcher QFT Module Documentation
+
+## Overview
+
+The Quantum Field Theory (QFT) module in WeightWatcher implements theoretical foundations from quantum field theory and renormalization group theory to analyze neural network weight matrices. This module is based on the theory that neural networks approach a critical point during training, which can be described as a kind of fractal where the free energy satisfies scale invariance, according to Wilson's exact renormalization group theory.
+
+## Theoretical Background
+
+### Critical Points in Neural Networks
+
+Neural networks exhibit behavior analogous to physical systems near critical points. At these critical points:
+
+1. **Scale Invariance**: Statistical properties remain unchanged across different scales
+2. **Power Law Distributions**: Eigenvalue/singular value distributions follow power laws
+3. **Long-Range Correlations**: Correlations decay as power laws rather than exponentially
+4. **Fractal Structure**: Self-similarity emerges across different scales
+
+### Wilson's Renormalization Group Theory
+
+The module implements concepts from Wilson's exact renormalization group theory:
+
+1. **Renormalization Flow**: Tracking how weight distributions evolve during training
+2. **Fixed Points**: Identifying stable, unstable, and critical fixed points in parameter space
+3. **Critical Exponents**: Measuring universal quantities that characterize phase transitions
+4. **Free Energy Landscape**: Mapping the thermodynamic properties of weight matrices
+
+## Key Features
+
+### 1. Critical Point Analysis
+
+```python
+from weightwatcher import RGAnalyzer
+
+# Initialize the analyzer
+rg_analyzer = RGAnalyzer()
+
+# Analyze a weight matrix
+results = rg_analyzer.analyze_critical_point(W)
+```
+
+This method analyzes how close a weight matrix is to a critical point by:
+- Estimating power law exponents from eigenvalue distributions
+- Measuring scale invariance properties
+- Determining if the matrix is near a critical point
+
+### 2. Fractal Dimension Analysis
+
+```python
+# Compute fractal dimension
+fractal_metrics = rg_analyzer.compute_fractal_dimension(W)
+```
+
+This method:
+- Estimates the fractal dimension using a box-counting approach
+- Quantifies self-similarity across scales
+- Provides metrics on the fractal properties of weight matrices
+
+### 3. Free Energy Landscape Mapping
+
+```python
+# Map free energy landscape
+energy_metrics = rg_analyzer.map_free_energy_landscape(W)
+```
+
+This method:
+- Calculates free energy based on eigenvalue spectrum
+- Computes entropy and energy
+- Identifies critical points in the free energy landscape
+- Measures specific heat capacity to detect phase transitions
+
+### 4. Renormalization Group Flow Tracking
+
+```python
+# Track RG flow across training epochs
+for epoch, weights in enumerate(weight_history):
+    metrics = rg_analyzer.track_rg_flow(weights, epoch=epoch)
+
+# Visualize the flow
+fig = rg_analyzer.visualize_rg_flow()
+```
+
+This functionality:
+- Tracks how weight matrices evolve during training
+- Visualizes the approach to criticality
+- Identifies when networks enter or leave critical regions
+
+### 5. Phase Transition Detection
+
+```python
+# Detect phase transitions between epochs
+transition = rg_analyzer.detect_phase_transition(W_before, W_after)
+```
+
+This method:
+- Detects significant changes in network behavior
+- Classifies transitions (to critical, from critical, non-critical)
+- Quantifies the magnitude of transitions
+
+### 6. Correlation Length Analysis
+
+```python
+# Analyze correlation length
+corr_metrics = rg_analyzer.analyze_correlation_length(W)
+```
+
+This method:
+- Estimates correlation length, which diverges at critical points
+- Calculates correlation decay exponents
+- Provides another measure of criticality
+
+### 7. Universality Class Classification
+
+```python
+# Classify into universality classes
+univ_class = rg_analyzer.compute_universality_class(W)
+```
+
+This method:
+- Classifies networks into known universality classes from statistical physics
+- Provides confidence scores for classifications
+- Helps understand universal properties of neural networks
+
+## Practical Applications
+
+### 1. Architecture Design
+
+The QFT module can guide architecture design by:
+- Identifying optimal initialization strategies that place networks near criticality
+- Suggesting layer sizes and connectivity patterns that promote scale invariance
+- Recommending regularization techniques that maintain critical behavior
+
+### 2. Training Optimization
+
+The module helps optimize training by:
+- Detecting when networks move away from critical regions
+- Identifying phase transitions that might indicate training issues
+- Suggesting learning rate adjustments based on RG flow
+
+### 3. Generalization Analysis
+
+The module provides insights into generalization by:
+- Correlating critical behavior with generalization performance
+- Identifying universality classes that tend to generalize better
+- Suggesting modifications to improve generalization based on QFT principles
+
+## Integration with Traditional WeightWatcher
+
+The QFT module complements traditional WeightWatcher metrics:
+
+| Traditional Metric | QFT Metric | Relationship |
+|-------------------|------------|--------------|
+| Alpha (power law) | Power Law Exponent | Directly related, but QFT provides theoretical foundation |
+| Stable Rank | Scale Invariance | Both measure effective dimensionality, but from different perspectives |
+| MP Fit | Free Energy | Both measure deviation from random matrices |
+| Layer Norm | Correlation Length | Both relate to the conditioning of weight matrices |
+
+## Example Usage
+
+```python
+import numpy as np
+from weightwatcher import RGAnalyzer
+
+# Initialize the analyzer
+rg_analyzer = RGAnalyzer(temperature=1.0)
+
+# Generate a synthetic weight matrix
+np.random.seed(42)
+W = np.random.normal(0, 1, (1000, 500))
+
+# Comprehensive analysis
+results = rg_analyzer.track_rg_flow(W)
+
+# Check if the matrix is near a critical point
+if results['is_critical']:
+    print("The weight matrix is near a critical point")
+else:
+    print("The weight matrix is far from criticality")
+
+# Print key metrics
+print(f"Power Law Exponent: {results['power_law_exponent']:.4f}")
+print(f"Scale Invariance: {results['scale_invariance']:.4f}")
+print(f"Fractal Dimension: {results['fractal_dimension']:.4f}")
+print(f"Free Energy: {results['free_energy']:.4f}")
+```
+
+## References
+
+1. Wilson, K.G. (1971). "Renormalization Group and Critical Phenomena"
+2. Martin, C.H. & Mahoney, M.W. (2019). "Traditional and Heavy-Tailed Self Regularization in Neural Network Models"
+3. Sornette, D. (2006). "Critical Phenomena in Natural Sciences"
+4. Mehta, P. & Schwab, D.J. (2014). "An exact mapping between the Variational Renormalization Group and Deep Learning"
+5. Roberts, D.A., Yaida, S., & Hanin, B. (2021). "The Principles of Deep Learning Theory"